Evidence map›Paper›PMID 32917193›Full record

ArticleBMC health services research2020

(Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports.

A Sheriffdeen, J L Millar, C Martin, M Evans, G Tikellis, S M Evans

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
5.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Perceptions and recall of treatment for prostate cancer: A survey of two populations.Technical innovations & patient support in radiation oncology · 2022
    Article
  9. Article
  10. Preventing Fragility Fractures: A 3-Month Critical Window of Opportunity.Geriatric orthopaedic surgery & rehabilitation · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

A SheriffdeenDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
J L MillarDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
C MartinDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
M EvansDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
G TikellisDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia.
S M EvansDepartment of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia. Sue.Evans@monash.edu.ORCID http://orcid.org/0000-0003-2962-8400
Monash University · AUMonash Health · AUThe Alfred Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBenchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the "truth". We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy.

methodsWe included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager's in each hospital, provided each patient's associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men.

resultsThe percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources.

conclusionRelying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient's disease profile. There does not appear to be a 'gold-standard' data source for the collection of data on comorbidities.

Indexed as

ComorbidityInternational Classification of DiseasesAgedAustraliaCohort StudiesHumansMaleMedical AuditMedical RecordsMiddle AgedProstatectomyProstatic NeoplasmsRetrospective StudiesSelf ReportComorbiditiesConcordanceProstate cancerSelf-reports

Identifiers

PMID32917193
PMCPMC7488579
OpenAlexW3084915203

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.